Interested in this AI/ML Engineer role at Instacart?
Apply Now →Skills & Technologies
About This Role
We're transforming the grocery industry
At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers.
Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.
Instacart is a Flex First team
There’s no one\-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in\-person events. Learn more about our flexible approach to where we work.
Why this role is on the menu
--------------------------------
The Legal department at Instacart is scaling rapidly across new business models, ad formats, and retail partnerships, and the systems and workflows that support it need to keep pace. Right now, legal operations, technology, and AI enablement are evolving in parallel without a single leader owning the strategy end\-to\-end. This role exists to close that gap: to build the infrastructure, tooling, and team capabilities that make the legal function faster, smarter, and more accountable. Twelve months from now, Instacart will have a modern contract lifecycle management system, a deployed AI enablement program with meaningful adoption, and a legal operations team with clear KPIs and the processes to sustain them. If you thrive at the intersection of law, operations, and technology, and want to build something that lasts, this role is for you
What you'll cook up in your first year
------------------------------------------
- A high\-performing legal operations team is built and running, with clear direction, performance management, and the capacity to serve the full organization.
- An AI enablement program is live and adopted: AI\-powered tools evaluated, deployed, and embedded into attorney workflows, with agentic automations reducing manual legal work and professionals upskilled across the team.
- Legal and business operations initiatives are executed and scaled, including Go\-To\-Market strategies, CPQ implementations and enhancements, and Ironclad workflows supporting new ad formats, retailer engagements, and other workstreams.
- Contract lifecycle management systems and cross\-system integrations are continuously improved, with cohesive collaboration across procurement, accounting, HR, sales, deal desk, IT, and engineering, and operational savings generated from Oracle, Zip, Salesforce, and Workday integrations with CLM and P2P workflows.
- Outside counsel operations are running with clear accountability: billing guidelines enforced, e\-billing systems managed, law firm evaluations complete, and spend reporting established.
- The team's knowledge management infrastructure is built and maintained, and KPI dashboards give leadership clear visibility into operational health.
The secret ingredients we're looking for
--------------------------------------------
Must\-have pantry staples (Minimum Qualifications)
- *8\+ years of experience in legal operations, strategy \& operations, or a related field, with demonstrated ownership of a legal operations program and at least 3 years of direct people management experience.*
- *Demonstrated experience leading at least two end\-to\-end technology implementations with deep systems integrations (e.g., Ironclad, Zip, Salesforce, CPQ, Oracle, Workday, or comparable platforms), including hands\-on experience owning AI tooling, evaluating vendors, deploying tools, and building or managing AI\-assisted or agentic workflows.*
- *Strong analytical and project management skills, with the ability to drive multiple complex initiatives simultaneously in a fast\-paced, evolving environment.*
- *Excellent communication and stakeholder management skills, with direct experience collaborating across legal, policy, finance, IT, and business teams.*
Optional garnishes (Preferred Qualifications)
- *Experience working in a high\-growth or recently public technology company with a complex, multi\-jurisdictional operational footprint, including exposure to marketplace or platform businesses.*
- *Experience designing agentic or automated legal workflows, with a bias toward building solutions before defaulting to off\-the\-shelf tools.*
- *Familiarity with legal operations communities (e.g., CLOC) or equivalent professional development, and experience conducting vendor RFPs or law firm evaluations with a focus on outside counsel cost discipline.*
- *Demonstrated ability to lead change management efforts that drive organizational adoption of new technology and processes across a distributed legal function.*
*\#LI\-Remote*
Instacart provides highly market\-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here.
Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.
For US based candidates, the base pay ranges for a successful candidate are listed below.
CA, NY, CT, NJ
$199,000 \- $210,000 USD
WA
$191,000 \- $201,500 USD
OR, DE, ME, MA, MD, NH, RI, VT, DC, PA, VA, CO, TX, IL, HI
$183,000 \- $193,000 USD
All other states
$166,000 \- $175,000 USD
Salary Context
This $183K-$201K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Instacart, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($192K) sits 11% below the category median. Disclosed range: $183K to $201K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Instacart AI Hiring
Instacart has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $201K - $201K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.